SoloQ: Calibration-Free Quantization for Diffusion Language Models

arXiv:2610.07121 · cs.LG · Submitted 2026-10-05 · Read on arXiv

cs.LG

Submitted: 2026-10-05

Updated: 2026-10-05

Code: https://github.com/Intelligent-Computing-Lab-Panda/SoloQ

Terminology

• Calibration — 1× in this paper · explained in 7 episodes
• Quantization — 1× in this paper · explained in 12 episodes

Sources

• GPT-4 Technical Report
• Program Synthesis with Large Language Models
• LLaDA2.0: Scaling Up Diffusion Language Models to 100B
• LLaDA2.1: Speeding Up Text Diffusion via Token Editing
• Evaluating Large Language Models Trained on Code
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Training Verifiers to Solve Math Word Problems
• GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
• Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
• PolarQuant: Quantizing KV Caches with Polar Transformation
• Measuring Massive Multitask Language Understanding
• Measuring Mathematical Problem Solving With the MATH Dataset
• OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
• ConvRot: Rotation-Based Plug-and-Play 4-bit Quantization for Diffusion Transformers
• FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers
• KronQ: LLM Quantization via Kronecker-Factored Hessian
• GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration
• KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

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